class_docstr: A :obj:`~gpytorch.variational._VariationalDistribution` that is defined to be a multivariate normal distribution
  with a diagonal covariance matrix. This will not be as flexible/expressive as a
  :obj:`~gpytorch.variational.CholeskyVariationalDistribution`.
  
  :param int num_inducing_points: Size of the variational distribution. This implies that the variational mean
      should be this size, and the variational covariance matrix should have this many rows and columns.
  :param batch_shape: Specifies an optional batch size
      for the variational parameters. This is useful for example when doing additive variational inference.
  :type batch_shape: :obj:`torch.Size`, optional
  :param float mean_init_std: (Default: 1e-3) Standard deviation of gaussian noise to add to the mean initialization.
code: def initialize_variational_distribution(self, prior_dist):
      self.variational_mean.data.copy_(prior_dist.mean)
      self.variational_mean.data.add_(
          torch.randn_like(prior_dist.mean), alpha=self.mean_init_std
      )
      self._variational_stddev.data.copy_(prior_dist.stddev)
  
file_path: gpytorch/gpytorch/variational/mean_field_variational_distribution.py
imports: import torch
  from linear_operator.operators import DiagLinearOperator
  from ..distributions import MultivariateNormal
  from ._variational_distribution import _VariationalDistribution
label_desc: Use image node_id 4 for calling the MeanFieldVariationalDistribution obj's underlying member method code with example usage: obj.initialize_variational_distribution(prior_dist) without return types